DarijaDB: Unlocking Text-to-SQL for Arabic Dialects

Recent advances in text-to-SQL models, which translate natural language questions (NLQs) into executable SQL queries, have made interacting with relational databases more accessible, even for those with limited technical ability. This task has seen significant improvement with the release of multiple English datasets and benchmarks such as WikiSQL, SPIDER, and BIRD, each covering different domains and levels of complexity. Non-English high-resource languages, such as Chinese, Russian, and Arabic, have also benefited from these advances, either through the translation of existing datasets or the creation of new ones. Dialect2SQL, a newly released text-to-SQL dataset, is dedicated to the Moroccan dialect (Darija), which is known for its complexity and distinctiveness compared to other Arabic dialects and Modern Standard Arabic. In this article, we conduct a comprehensive study on text-to-SQL for Darija by conducting several experiments mainly on the Dialect2SQL dataset using different approaches and configurations with two code-based large language models, StarCoder2 and Qwen-2.5-Coder. The experiments reveal the performance gap between models fine-tuned on English data and those fine-tuned on Darija. Additionally, the results illustrate the positive impact of incorporating multi-language datasets during training. In particular, the gap decreases from 10.1% to 6.7% in BLEU, and from 12.5% to 5.7% in TSED.